Analysis Methodology
print("📊 Analysis Methodology:")
print("=" * 40)
print("1. Exploratory Data Analysis (EDA)")
print(" - Data profiling and quality assessment")
print(" - Descriptive statistics and distributions")
print(" - Missing value and outlier detection")
print()
print("2. Statistical Testing & Inference")
print(" - Correlation analysis between key variables")
print(" - T-tests for demographic differences")
print(" - Distribution analysis (skewness, kurtosis)")
print()
print("3. Advanced Analytics")
print(" - Customer segmentation analysis")
print(" - Behavioral pattern identification")
print(" - Risk assessment and satisfaction analysis")
print()
print("4. Business Intelligence")
print(" - Interactive visualizations (Plotly)")
print(" - Statistical plots (Seaborn, Matplotlib)")
print(" - Data-driven recommendations")
print()
print("5. Tools & Technologies")
print(" - Python: Pandas, NumPy, Scipy")
print(" - Visualization: Plotly, Seaborn, Matplotlib")
print(" - Statistical Analysis: Hypothesis testing, correlation")📊 Analysis Methodology:
========================================
1. Exploratory Data Analysis (EDA)
- Data profiling and quality assessment
- Descriptive statistics and distributions
- Missing value and outlier detection
2. Statistical Testing & Inference
- Correlation analysis between key variables
- T-tests for demographic differences
- Distribution analysis (skewness, kurtosis)
3. Advanced Analytics
- Customer segmentation analysis
- Behavioral pattern identification
- Risk assessment and satisfaction analysis
4. Business Intelligence
- Interactive visualizations (Plotly)
- Statistical plots (Seaborn, Matplotlib)
- Data-driven recommendations
5. Tools & Technologies
- Python: Pandas, NumPy, Scipy
- Visualization: Plotly, Seaborn, Matplotlib
- Statistical Analysis: Hypothesis testing, correlation